Evolving landscape of imaging-based evaluation in systemic autoimmune rheumatic disease-associated interstitial lung disease: from visual assessment to quantitative artificial intelligence-assisted evaluation.

Interstitial lung disease (ILD) is a major driver of morbidity and mortality across systemic autoimmune rheumatic diseases (SARDs), with systemic sclerosis-associated ILD (SSc-ILD) providing the most extensive evidence base. In this context, progressive pulmonary fibrosis has emerged as a central framework, as it is associated with increased mortality and facilitates the identification of candidates for antifibrotic therapy. Nevertheless, operational thresholds for chest high-resolution computed tomography (HRCT)-defined progression remain ill defined: current guidelines rely on visual HRCT interpretation and lack standardized, reproducible assessment protocols. Because the magnitude and topography of disease evolution can guide therapeutic decisions, quantitative evaluation of imaging features is pivotal. In this review, we delineate the evolution of imaging assessment from qualitative reads to quantitative phenotyping. We organize traditional densitometric and textural metrics (e.g., percentage high-attenuation areas, quantitative lung fibrosis, CALIPER [Computer-Aided Lung Informatics for Pathology Evaluation and Ratings]) alongside hybrid/data-driven approaches (e.g., data-driven textural analysis, quantitative interstitial abnormality) and recent deep-learning tools (e.g., SOFIA [Systemic Objective Fibrotic Imaging Analysis Algorithm], eLung, Qureight, SATORI [Segmentation and Annotation Tool for Radiomics and Deep Learning], AirQuant). Given the rapid pace of innovation in artificial-intelligence-based quantitative CT, we present a curated set of analytic approaches and offer a concise framework for understanding technological progress and evaluating its relevance to SARD-ILD applications.